Paula Fischer

Zusammenfassung

Industrial prognostics and health management (PHM) increasingly relies on vibration-based deep learning, yet field deployment remains limited by two practical constraints: (i) fault labels are scarce and expensive, and (ii) models trained on one machine or dataset often degrade under distribution (domain) shift (different sensors, sampling rates, loads, and signal conventions). These constraints motivate vibration foundation models: reusable encoders trained once on large collections of unlabeled raw vibration recordings and adapted to new assets with minimal supervision. This paper presents VibFM, a Transformer encoder trained via self-supervised masked spectrogram modeling in the spirit of masked language modeling and masked autoencoders. Raw waveforms from 16 open datasets totaling ≈ 400 hours are standardized into 128 × 128 log-magnitude short-time Fourier transform (STFT) spectrograms and paired with a compact conditioning vector that encodes sampling rate and time/frequency resolution. Pre-training reconstructs masked time–frequency patches, encouraging the encoder to capture transferable vibration primitives such as persistent narrowband ridges, modulation sidebands, and impulsive transients. Transfer is evaluated on the held-out Paderborn University and KAt DataCenter bearing benchmark (excluded from pre-training) using leakage-resistant bearing-level splits. On three-class fault diagnosis, frozen VibFM features substantially improve over training from scratch, while end-to-end fine-tuning provides the strongest performance. For reconstruction-based anomaly detection, adapting a decoder on healthy target data yields reconstruction-error scores that separate healthy from damaged states across operating conditions. Masked reconstructions and pooling-attention visualizations provide qualitative audits of learned time–frequency structure, and the limits of these interpretability probes are discussed.

Zusammenfassung

The simulation study effectively demonstrates the feasibility of different maintenance strategies. The derivation of regression equations for all three maintenance strategies - reactive maintenance (RM), predictive maintenance (PM), and preventive maintenance (IM)-1 provides valuable insights into the cost implications of each strategy across a wide range of parameter combinations. These regression equations enable calculating costs for each operational strategy based on various input parameters. It is, therefore, possible to calculate which operational strategy is likely to be the most cost-effective under the given conditions, providing clarity on whether the implementation of a predictive maintenance strategy can be successful. A case study shows that when using predictive maintenance with RUL (Remaining Useful Life) prediction, the prediction quantile that determines the maintenance timing should be chosen at a low level. It was also shown that, for certain cost ratios between predictive costs and reactive costs, the range within which predictive maintenance can work under given circumstances can be very narrow (or even nonexistent).